AIM:To explore the method for early diagnosis of gastric cancer by screening the expression spectrum of saliva protein in gastric cancer patients using mass spectrometry for proteomics.METHODS:Proportional peptide mas...AIM:To explore the method for early diagnosis of gastric cancer by screening the expression spectrum of saliva protein in gastric cancer patients using mass spectrometry for proteomics.METHODS:Proportional peptide mass fingerprints were obtained by analysis based on proteomics matrix-assisted laser desorption ionization time-of-flight/mass spectrometry.A diagnosis model was established using weak cation exchange magnetic beads to test saliva specimens from gastric cancer patients and healthy subjects.RESULTS:Significant differences were observed in the mass to charge ratio(m/z) peaks of four proteins(1472.78 Da,2936.49 Da,6556.81 Da and 7081.17 Da) between gastric cancer patients and healthy subjects.CONCLUSION:The finger print mass spectrum of saliva protein in patients with gastric cancer can be established using gastric cancer proteomics.A diagnostic model for distinguishing protein expression mass spectra of gastric cancer from non-gastric-cancer saliva can be established according to the different expression of proteins 1472.78 Da,2936.49 Da,6556.81 Da and 7081.17 Da.The method for early diagnosis of gastric cancer is of certain value for screening special biological markers.展开更多
Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach...Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.展开更多
基金Supported by The National Natural Science Foundation of China,No. 30640071
文摘AIM:To explore the method for early diagnosis of gastric cancer by screening the expression spectrum of saliva protein in gastric cancer patients using mass spectrometry for proteomics.METHODS:Proportional peptide mass fingerprints were obtained by analysis based on proteomics matrix-assisted laser desorption ionization time-of-flight/mass spectrometry.A diagnosis model was established using weak cation exchange magnetic beads to test saliva specimens from gastric cancer patients and healthy subjects.RESULTS:Significant differences were observed in the mass to charge ratio(m/z) peaks of four proteins(1472.78 Da,2936.49 Da,6556.81 Da and 7081.17 Da) between gastric cancer patients and healthy subjects.CONCLUSION:The finger print mass spectrum of saliva protein in patients with gastric cancer can be established using gastric cancer proteomics.A diagnostic model for distinguishing protein expression mass spectra of gastric cancer from non-gastric-cancer saliva can be established according to the different expression of proteins 1472.78 Da,2936.49 Da,6556.81 Da and 7081.17 Da.The method for early diagnosis of gastric cancer is of certain value for screening special biological markers.
文摘Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.
